Publication Abstract
Mining Temporally-Varying Phenomena in Scientific Datasets
Machiraju, R., Parthasarathy, S., Wilkins, J., Thompson, D., Gatlin, B., Richie, D., Choy, T., Jiang, M., Mehta, S., Coatney, M., Barr, S., & Hazzard, K. (2004). Mining Temporally-Varying Phenomena in Scientific Datasets. In H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha (Eds.), Data Mining: Next Generation Challenges and Future Directions. Cambridge, MA: AAAI Press/The MIT Press. 273-290.
Abstract
Simulation is enhancing and, in many instances, replacing experimentation as a means
to gain insight into complex physical phenomena. Recent advances in computer hardware
and numerical methods have made it possible to simulate physical phenomena at
very fine temporal and spatial resolutions. Unfortunately, given the enormous sizes of
the datasets involved, analyzing datasets produced by these simulations is extremely
challenging. In order to more fully exploit simulation, the analysis of these large
datasets must advance beyond current techniques that are based on interactive visualization.
We outline our vision for one such approach and describe progress on a unified
framework that promises to provide a novel method to explore large simulation datasets. We illustrate its application to two disparate science drivers – temporally varying
solid and fluid systems. In both applications, there are hidden hierarchies of features
as well as many abstract multidimensional feature characterizations (e.g. shapes).
Through this framework, we offer a systematic approach to detect, characterize, and
track meta-stable features as well as formulate hypotheses about their evolution – an
important step in extracting vital information from such complex systems.